Utilizing Oil-Soluble Tracers to Evaluate the Production Profile in Multistage Fractured Horizontal Wells
Multistage hydraulic fracturing along a horizontal well is the key to effectively recover hydrocarbon from tight reservoirs. Improving the hydrocarbon recovery requires detailed production information of each hydraulic fracture. Chemical watersoluble tracers are often used to calculate the production profile from multistage fracturing through tracer flow back test. Unlike conventional water-soluble tracers that are in the form of a liquid, oil-soluble tracers are embedded in the porous media and absorbed on the surfaces of solid carrier particulates. The unique characteristic of oilsoluble tracers is that the tracer will only be released from its carrier particulate when oil passes through and has negligible partitioning into the water or gas phase. Therefore, oil-soluble tracers are used as an inexpensive and reliable indicator that can indirectly estimate the oil production contribution in individual fracture stages. It is widely assumed that the ratio of tracer production per stage over total tracer production represents the same ratio of oil production per stage over total oil production. However, deviations have been found between the two ratios. This study is to analyze factors affecting the accuracy of utilizing oil-soluble tracer to estimate the oil contribution per fracture stage. Referencing a selected Broadview well in the Wainwright Sparky formation, the horizontal well with 22 multistage hydraulic fractures was simulated using CMG-STARS. The simulated model was first history matched to validate the reservoir parameters. A sensitivity analysis was then performed to determine the dominating factors that influenced the accuracy of using oil-soluble tracers to estimate the production contribution from each fracture stage. Correlations were derived based on the sensitivity results to reveal the relationship between oil and tracer production profile at the most sensitising parameters. Finally, a feed-forward neural network model with back-propagation error algorithm was coded in Matlab to estimate the cumulative oil production ratio when there is a big database of known input parameters of the same reservoir. The comparison between predicted values obtained from the artificial neural network model and target values from the sensitivity analysis demonstrated the effectiveness and potential of the artificial neural network model at estimating the oil production profile.
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Faculty of Graduate Studies and Research, University of Regina